Machine learning?

[microbuns]

Machine learning would have a tough time distinguishing a hopper unless you have a near perfect replica of the hopper on your practice field to get training data from. Make sure if you do try to build as visually similar hopper as you can. Definitely an interesting idea, would love to hear the results if you try it!

[David Lame]

"Machine Learning" covers a lot of ground, but there are aspects that might work. Heavy emphasis on "might".

On the web you can find a whole lot of tutorials on using a Haar Cascade to recognize faces and other things---but mostly faces, all using opencv My guess is that a hopper has enough similarity to a face that it would be a good candidate. I can't claim to have tried it. My own experience with Haar Cascade training is pretty limited, but it worked with faces. On the other hand, I never got it working with last year's boulders.

To the last post's point, you will need several examples of a real picture of a real hopper.


On the other hand, if your goal is to recognize a hopper, as opposed to studying machine learning, some more traditional HSV filtering, dilation and selection might do a better job of recognizing a hopper. There aren't many big yellow-green areas at hopper height. My own goal is to use that method late in the season to try and dump a hopper during autonomous, but that's weeks ahead. At our first competition I will just be recording video to use as training images.